Structural Health Monitoring of Existing Bridges using Bridge Weigh-in-motion Measurements – Value of Information Analysis

2018 ◽  
Author(s):  
Dominik Skokandic ◽  
Ana Mandić Ivanković ◽  
Aleš Žnidarič
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Value Of Information ◽  
Information Analysis ◽  
Structural Health ◽  
Value Of Information Analysis ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion ◽  
Motion Measurements ◽  
Existing Bridges

scholarly journals Value of Information of Structural Health Monitoring in Asset Management of Flood Defences

2019 ◽  
Vol 4 (3) ◽  
pp. 56 ◽  
Author(s):  
Wouter Jan Klerk ◽  
Timo Schweckendiek ◽  
Frank den Heijer ◽  
Matthijs Kok
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Asset Management ◽  
Value Of Information ◽  
Relative Magnitude ◽  
Practical Implementation ◽  
Structural Health ◽  
Monitoring Strategies ◽  
Efficient Measure ◽  
Flood Defence

One of the most rapidly emerging measures in infrastructure asset management is Structural Health Monitoring (SHM), which aims at reducing uncertainty in structural performance by using monitoring equipment. As earthen flood defence structures typically have large strength uncertainties, such techniques can be particularly promising. However, insight in the key characteristics for successful SHM for flood defences is lacking, which hampers the practical implementation. In this study, we explore the benefits of pore pressure monitoring, one of the most promising SHM techniques for earthen flood defences. The approach is based on a Bayesian pre-posterior analysis, and results are evaluated based on the Value of Information (VoI) obtained from different monitoring strategies. We specifically investigate the effect on long-term reinforcement decisions. The results show that, next to the relative magnitude of reducible uncertainty, the combination of the probability of having a useful observation and the duration of a SHM effort determine the VoI. As it is likely that increasing loads due to climate change will result in more frequent future reinforcements, the influence of scenarios of different rates of increase in future loads is also investigated. It was found that, in all considered possible scenarios, monitoring yields a positive Value of Information, hence it is an economically efficient measure for flood defence asset management both now and in the future.

Structural Health Monitoring of Existing Bridges in Earthquake Prone Areas: Laboratory Validation

2017 ◽  
Author(s):  
CARLO RAINIERI ◽  
DANILO GARGARO ◽  
GIOVANNI FABBROCINO ◽  
LUIGI DI SARNO ◽  
GIUSEPPE MADDALONI ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Existing Bridges

Value of information of structural health monitoring with temporally dependent observations

2021 ◽  
pp. 147592172110306
Author(s):  
Jannie S Nielsen
Keyword(s):  
Structural Health Monitoring ◽  
Numerical Experiment ◽  
Bayesian Network ◽  
Health Monitoring ◽  
Value Of Information ◽  
Decision Model ◽  
Parameter Study ◽  
Maintenance Planning ◽  
Structural Health ◽  
Dependent Observations

A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.

scholarly journals Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis

2018 ◽  
Vol 18 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Shojaeddin Jamali ◽  
Tommy HT Chan ◽  
Andy Nguyen ◽  
David P Thambiratnam
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Carrying Capacity ◽  
Assessment Procedure ◽  
Load Rating ◽  
Load Carrying Capacity ◽  
Structural Health ◽  
Load Carrying ◽  
Existing Bridges ◽  
Bridge Assessment

For assessment of existing bridges, load rating is usually performed to assess the capacity against vehicular loading. Codified load rating can be conservative if the rating is not coupled with the field data or if simplifications are incorporated into assessment. Recent changes made to the Australian Bridge assessment code (AS 5100.7) distinguish the difference between design and assessment requirements, and include addition of structural health monitoring for bridge assessment. However, very limited guidelines are provided regarding higher order assessment levels, where more refined approaches are required to optimize the accuracy of the assessment procedure. This article proposes a multi-tier assessment procedure for capacity estimation of existing bridges using a combination of structural health monitoring techniques, advanced nonlinear analysis, and probabilistic approaches to effectively address the safety issues on aging bridges. Assessment of a Box Girder bridge was carried out according to the proposed multi-tier assessment, using data obtained from modal and destructive testing. Results of analysis at different assessment tiers showed that both load-carrying capacity and safety index of the bridge vary significantly if current bridge information is used instead of as-designed bridge information. Findings emerged from this study demonstrated that accuracy of bridge assessment is significantly improved when structural health monitoring techniques along with reliability approaches and nonlinear finite element analysis are incorporated, which will have important implications that are relevant to both practitioners and asset managers.

scholarly journals An alternative quantification of the value of information in structural health monitoring

2021 ◽  
pp. 147592172110284
Author(s):  
Mayank Chadha ◽  
Zhen Hu ◽  
Michael D Todd
Keyword(s):  
Structural Health Monitoring ◽  
Decision Analysis ◽  
Health Monitoring ◽  
Structural State ◽  
Value Of Information ◽  
Design Research ◽  
Cost Benefit Analysis ◽  
Information Gathering ◽  
Structural Health ◽  
System Designs

Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.

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